Category: Data Governance

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A Congressional Trade Office could resolve all this tariff confusion

 Susan Ariel Aaronson

Congress shares responsibility with the president for trade policy, yet Congress lacks the infrastructure and expertise to set objectives and monitor the administration’s actions. Moreover, because President Trump sees tariffs as his Swiss Army knife for multiple purposes, Congress has been unable to effectively challenge the dramatic changes to trade policy made by the administration. 

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Do Chatbot Developers Act Responsibly toward their Users?

 Susan Ariel Aaronson and Michael Moreno

Please note this paper is forthcoming, to be published by the Balsillie School in February 2026. 

This study evaluates whether leading AI developers—OpenAI, Google, xAI, and DeepSeek—act responsibly toward users when building and deploying chatbots. We assess how each firm defines and implements responsibilities related to user rights and welfare through website claims, technical documentation, and chatbot behavior. Using a mixed-methods comparison, we find that companies publicly emphasize safety but inconsistently discuss accountability, transparency, and protections for users at risk of harm. Technical documents largely explain model architecture rather than how developers mitigate risks to vulnerable users, while chatbot responses vary over time and across firms in addressing self-harm, privacy, and abusive content. Our findings show that AI responsibility remains fragmented, revealing opportunities for policymakers to establish clearer norms for protecting user rights and welfare.

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AI and Trade: The WTO’s Thoughtful but Incomplete Assessment

When the World Trade Organization (WTO) decided in 2024 to produce a report on the trade implications of artificial intelligence (AI), it set out to answer two key questions: How can the WTO help ensure that the benefits of AI are widespread? And, how can the challenges surrounding AI be addressed in a globally coordinated manner? This paper analyses the WTO’s findings and explores how AI is prompting a re-examination of trade rules as well as how and what nations trade. Following an analysis of the trade and technology relationship, the author then discusses how AI poses challenges to how trade is conducted and the current rules system. The author’s recommendations for the WTO include partnering with civil society groups that have expertise in competition policy and data governance; doing research on trade-related global issues; and using trade policy reviews to better understand global data markets.

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Public Concerns About AI Are Getting Lost in Translation

Artificial intelligence (AI) is playing an increasingly important role in people’s lives, which has led to growing calls for AI regulation. Although the public has strong opinions on AI, getting policy makers to listen often leads to citizens’ concerns getting “lost in translation.” This paper looks at whether and how three countries (Australia, Colombia and the United States) sought public opinion on AI governance and offers recommendations on how to improve citizen engagement in AI policy making. The authors found that none of the three countries made much effort to seek public input, resulting in less than one percent of their respective populations participating in the consultative process.

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Do AI Chatbot Firms Practice What They Preach?

This study examines whether leading AI chatbot companies implement the responsible AI principles they publicly advocate. The authors used a mixed-methods approach analyzing four major chatbots (ChatGPT, Gemini, Deep Seek, and Grok) across company websites, technical documentation, and direct chatbot evaluations. We found significant gaps between corporate rhetoric and practice.
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China-U.S. Rivalry Will Split the World into Competing AI Camps

The U.S. and China both put forward plans for artificial intelligence last month. The two have long sought to lead on AI, and their competition has led to technological breakthroughs, lower costs, and wider use of the technology. But as their new plans illustrate, that competition may also divide the world into competing realms of AI products and governance.

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Taking the Wrong Lesson from China’s AI Strategy

Taking the Wrong Lesson from China’s AI Strategy

The United States is mimicking China’s approach to centralized data, risking privacy, security and democratic accountability in the name of AI leadership.

Chinese leaders early on recognized the importance of data for artificial intelligence (AI). In 2015, officials announced incentives to treat data as a key factor of production. More recently, China became the first country to create official markets for data. Chinese policy makers developed standards that enabled private entities to claim data as an asset on balance sheets. But China may have made a data “misstep.” In 2014, Beijing announced that it would allow government agencies to share data about Chinese companies and citizens, create a centralized scoring mechanism to assess the financial creditworthiness of individuals and companies, and use that data to nudge citizens toward state-sanctioned “moral values.” Observers noted that the plan has yielded mixed results, and spillover effects such as corruption and distrust. Nonetheless, on April 1, 2025, the government announced new guidelines on the social credit system, intended to create a “unified national market.”

Although the Trump administration has worked to thwart China’s AI leadership, it is copying some aspects of China’s data policies by creating a unified federal data set.

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Regrets of the Tech Bros: In a land ruled by the law of the jungle

On his Inauguration Day, Donald Trump sent a message. The founders and CEOs of Apple,Amazon, Google, Meta, Open AI, and Uber, among other giant high-tech companies, sat in the front rows near the Trump family and cabinet nominees. Trump and his staff wanted to use that display of innovation, money, and power to convey that he was supported by America’s most successful companies. In turn, these leaders hoped the administration would ease the regulation of data-driven technologies and the business practices (surveillance capitalism) followed by many of these giant firms.

However, some two months later, many of these tech bros may regret their unabashed support for the future Trump
promised.

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Data Disquiet: Concerns about the Governance of Data for Generative AI

The growing popularity of large language models (LLMs) has raised concerns about their accuracy. These chatbots can be used to provide information, but it may be tainted by errors or made-up or false information (hallucinations) caused by problematic data sets or incorrect assumptions made by the model. The questionable results produced by chatbots has led to growing disquiet among users, developers and policy makers. The author argues that policy makers need to develop a systemic approach to address these concerns. The current piecemeal approach does not reflect the complexity of LLMs or the magnitude of the data upon which they are based, therefore, the author recommends incentivizing greater transparency and accountability around data-set development.

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How to Regulate AI? Start With the Data

We live in an era of data dichotomy. On one hand, AI developers rely on large data sets to “train” their systems about the world and respond to user questions. These data troves have become increasingly valuable and visible. On the other hand, despite the import of data, U.S. policy makers don’t view data governance as a vehicle to regulate AI.

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